Understanding Singular Linear Maps: R^m -> R^n

In summary, the conversation discusses the definition of a singular linear map and the different ways in which it can fail to have an inverse. It also mentions the representation of a linear map by matrices and the topic of linear algebra. The person asking the question is advised to study linear algebra for a better understanding of the subject.
  • #1
annoymage
362
0

Homework Statement



the question here said

is L, linear transformation/mapping is singular?

i'm still googling the definition singular linear map,

can anyone give me the definition please T_T

p/s; i thought it L maybe the matrix representation, but the question

L : R^m -> R^n

but aren't matrix representation are define on L: M(m,1) -> M(n,1)

should i take transpose of R^m and R^n ?
 
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  • #2
If L is a linear map between finite-dimensional vector spaces (such as [itex]\mathbb{R}^m[/itex] and [itex]\mathbb{R}^n[/itex]), then it can be represented by a matrix. In fact, it can be represented by many different matrices, each corresponding to different choices of basis. But sometimes the answer to a question can be clearer if you don't focus on the matrix representation. Unfortunately, you did not fully state the question! (i.e., how is L defined?)

In any case, a singular linear map is simply one that does not have an inverse. It can fail to have an inverse in one (or both) of two ways: (1) it is not injective, meaning there exist [itex]a \neq b \in \mathbb{R}^m[/itex] such that [itex]L(a) = L(b)[/itex]; or (2) it is not surjective, meaning that there exists some [itex]b \in \mathbb{R}^n[/itex] such that [itex]L(a) \neq b[/itex] for all [itex]a \in \mathbb{R}^m[/itex].

If [itex]m \neq n[/itex], then [itex]L[/itex] is guaranteed to be singular. (Why?)
 
  • #3
thanks for the definition.

And your question, I can see If m<n then not surjective means singular

if m>n then not injective means singular. But still working on that. Tomorrow i'll try to post, help check it.

Anyway, i don't understand this,
jbunniii said:
If L is a linear map between finite-dimensional vector spaces (such as [itex]\mathbb{R}^m[/itex] and [itex]\mathbb{R}^n[/itex]), then it can be represented by a matrix. In fact, it can be represented by many different matrices, each corresponding to different choices of basis.

what topic should i study for this thing?
 
  • #4
annoymage said:
And your question, I can see If m<n then not surjective means singular

Correct, surjectivity is impossible when [itex]m < n[/itex], and injectivity is impossible when [itex]m > n[/itex].
Anyway, i don't understand this

what topic should i study for this thing?

Well, the topic is linear algebra, but you can study it at several levels of sophistication. Are you taking a course in it? If so, have you seen the definition of a linear map between two vector spaces? It should look something like this:

If V and W are vector spaces over the same field K and [itex]L : V \rightarrow W[/itex], then L is a linear map if the following are true for all [itex]v, v_1, v_2 \in V[/itex], and [itex]k \in K[/itex]:

(1) [itex]L(v_1 + v_2) = L(v_1) + L(v_2)[/itex]

(2) [itex] L(kv) = kL(v)[/itex]
 
  • #5
annoymage said:
what topic should i study for this thing?

doesn't matter, i think i found it, i'll read it forthwith
 
  • #6
jbunniii said:
Well, the topic is linear algebra, but you can study it at several levels of sophistication. Are you taking a course in it? If so, have you seen the definition of a linear map between two vector spaces? It should look something like this:

If V and W are vector spaces over the same field K and [itex]L : V \rightarrow W[/itex], then L is a linear map if the following are true for all [itex]v, v_1, v_2 \in V[/itex], and [itex]k \in K[/itex]:

(1) [itex]L(v_1 + v_2) = L(v_1) + L(v_2)[/itex]

(2) [itex] L(kv) = kL(v)[/itex]

yea i did pass that topic, only my lecture note doesn't define what singularity of linear map is, anyway thank you so much
 

1. What is a singular linear map?

A singular linear map is a function that maps vectors from one vector space to another, where the two vector spaces have different dimensions and the map is not invertible. It can also be referred to as a non-injective or non-surjective linear map.

2. How is a singular linear map represented?

A singular linear map is represented by a matrix with more columns than rows, as the dimension of the input vector space is smaller than the dimension of the output vector space. The entries in the matrix correspond to the coefficients of the linear transformation on the input vector.

3. What is the difference between a singular linear map and a non-singular linear map?

A non-singular linear map is invertible, meaning it has an inverse function that maps the output vectors back to the input vectors. A singular linear map, on the other hand, is not invertible and therefore does not have an inverse function.

4. What are some real-world applications of singular linear maps?

Singular linear maps are commonly used in data compression, where the input data (such as an image) is mapped to a lower-dimensional space to reduce its size. They are also used in signal processing and image processing, where the input signal or image is transformed in some way before being analyzed or reconstructed.

5. How can I determine if a linear map is singular?

A linear map is singular if and only if its determinant is equal to 0. This can be found by taking the determinant of its corresponding matrix representation. If the determinant is 0, the map is singular; if it is non-zero, the map is non-singular.

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